Abstract
Ellipse detection is a basic task in many computer-vision related problems. While widely studied in recent years, accurate and efficient detection in real-world images is still a challenge. In this paper, a novel ellipse detector, with high accuracy and efficiency, is proposed. The detector models edge by block sequences, and extracts a set of elliptical arcs, which are classified into four sets. Then top-down ellipse fitting strategy that also makes the method able to detect small and flat ellipses is designed. A two-level validation process is used to select highly probable potential ellipses, especially for fragmented ellipses. Experiments on four synthetic datasets show that the proposed method performs far better than existing methods. In images with severe cluttering and occlusion, the F-measure can still be around 0.9. On four real image datasets the proposed method achieves better F-measure scores with competitive speed than state-of-the-art techniques.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.